Gathering Global Perspectives to Establish the Research Priorities and Minimum Data Sets for Degenerative Cervical Myelopathy: Sampling Strategy of the First Round Consensus Surveys of AO Spine RECODE-DCM
Bibliographic record
Abstract
STUDY DESIGN: Survey. INTRODUCTION: AO Spine Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy (AO Spine RECODE-DCM) is an international initiative that aims to accelerate knowledge discovery and improve outcomes by developing a consensus framework for research. This includes defining the top research priorities, an index term and a minimum data set (core outcome set and core data elements set - core outcome set (COS)/core data elements (CDE)). OBJECTIVE: To describe how perspectives were gathered and report the detailed sampling characteristics. METHODS: A two-stage, electronic survey was used to gather and seek initial consensus. Perspectives were sought from spinal surgeons, other healthcare professionals and people with degenerative cervical myelopathy (DCM). Participants were allocated to one of two parallel streams: (1) priority setting or (2) minimum dataset. An email campaign was developed to advertise the survey to relevant global stakeholder individuals and organisations. People with DCM were recruited using the international DCM charity Myelopathy.org and its social media channels. A network of global partners was recruited to act as project ambassadors. Data from Google Analytics, MailChimp and Calibrum helped optimise survey dissemination. RESULTS: Survey engagement was high amongst the three stakeholder groups: 208 people with DCM, 389 spinal surgeons and 157 other healthcare professionals. Individuals from 76 different countries participated; the United States, United Kingdom and Canada were the most common countries of participants. CONCLUSION: AO Spine RECODE-DCM recruited a diverse and sufficient number of participants for an international PSP and COS/CDE process. Whilst PSP and COS/CDE have been undertaken in other fields, to our knowledge, this is the first time they have been combined in one process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".